Trend: The normal curve may not be the best model for predicting market behavior in the future.
John Hagel writes in his blog that our reality is shifting from a "normal curve" (Gaussian) world to an "80/20 rule" (Pareto) world, with profound implications for business. He cites work by McKelvey and Andriani about the need to change our mindsets to adapt. Excerpts below.
In a world of power law or Pareto distributions, extreme events become much more prominent. Extreme events can take many forms. They can be sudden and severe disturbances like a class 9 earthquake or a financial meltdown like the one that occurred in US stock markets in 1987. As McKelvey and Andriani observe, “the lesson that we can draw . . . is that extreme events, which in a Gaussian world could be safely ignored, are not only more common than expected but also of vastly larger magnitude and far more consequential.”
Our institutions (not just businesses, but also educational and governmental) are largely designed for a Gaussian world where averages and forecasts are meaningful. As a result, we have evolved a sophisticated set of push programs that have delivered significant efficiency. In a world of sudden, severe and difficult to anticipate shifts, push programs become much less viable and we need to become a lot more creative in terms of designing pull platforms.
...companies like Google and Microsoft have achieved enormous concentration of economic value creation that defies the averages of the Gaussian world. These extreme events have an interesting property – they emerge first in the “fat tail”, on the edge of conventional business activity, driven by a different view of business opportunity, and then gather momentum until they eventually break into the head of the distribution and change the game for everyone else. The challenge for business managers is to sort out the signal from the noise in the fat tail and spot early on the emergent extreme events that could reshape the business landscape. The Gaussian focus on averages obscures these events, treating them as meaningless
“outliers” until it is too late.
There’s another form of extreme event that also becomes more prominent in a Paretian world – this is the tendency for extreme forms of clustering in social networks, whether it takes the form of clustering in mega-cities in physical space or clustering of links and traffic on web sites in virtual space. Economic value inexorably follows these social clusters. This also has powerful implications for business, ranging from where to locate operations in physical space to how to redesign institutional architectures to accommodate thousands of business partners. There’s also a public policy implication – in many domains we are likely to see degrees of concentration and consolidation of economic power that is unprecedented.
The problem is that most of our analytical tools are designed to understand Gaussian worlds. These same tools seriously miss, or even distort, the dynamics of Paretian worlds. We need an entirely new analytical tool kit for the Paretian world.
The rewards for achieving a better understanding of the Paretian world are enormous. Small moves, smartly made, can lead to exponential improvements in wealth creation provided they leverage the deep structures that define Paretian distributions. In contrast to the scaling strategies described earlier in the Gaussian world, different and even more powerful scaling strategies become feasible in the Paretian world, converting instability from a liability into an advantage.
There is a natural and very human tendency to seek out the typical or the average and to search for more predictability. By implication, a Paretian world requires a much more dynamic view of the world, one that looks for patterns in evolving relationships, rooted deeply in context, and that understands how these changing patterns reshape who we are as well as our opportunities for growth.